In the digital age, information is king, but only if you can find it. The way we search for and retrieve data has evolved dramatically over the years, moving from simple metadata tagging to sophisticated artificial intelligence (AI)-powered search engines. This transformation has not only improved the speed and accuracy of finding information but has also reshaped how we interact with digital content.

Before AI, searchability relied heavily on metadata—structured data that describes other data. Websites and digital files followed the same principle, using keywords, descriptions and structured data to enhance searchability.

However, this approach had its limitations. Metadata was often manually assigned, which left room for inconsistencies, human error and subjectivity. As search engines became more sophisticated, they began leveraging algorithms to improve findability. This shift marked the beginning of AI’s involvement in searchability.

Today, AI has taken findability to new heights. Natural language processing (NLP) enables search engines to understand human language with near-human accuracy. AI-driven tools like Google’s BERT (Bidirectional Encoder Representations from Transformers) and OpenAI’s GPT models can grasp the context behind queries, making searches more effective than ever before.

When it comes to findability, taxonomies still play a crucial role in organizing and classifying information into structured hierarchies, allowing users to navigate content more efficiently. By grouping related topics and creating standardized categories, taxonomies help improve the accuracy of metadata and made it easier to retrieve relevant information.

As AI continues to evolve, findability will become even more seamless and intuitive. Emerging technologies like multimodal AI (which processes text, images, and voice together) and hyper-personalization will further enhance search experiences. Additionally, as AI systems become more explainable and transparent, users will have greater trust in their recommendations and search results.

From metadata-driven search to AI-powered discovery, the way we find information has come a long way. What once required meticulous tagging and manual indexing is now an intelligent, adaptive system that learns and improves over time.

Despite the rapid advancements in AI, taxonomy-based search remains indispensable for ensuring accurate, logical and human-intuitive search results. AI may provide speed and automation, but taxonomies offer the structure and reliability that modern organizations need to maintain clarity and consistency in searchability. As search technology evolves, a hybrid approach that integrates AI with well-structured taxonomies will likely offer the best of both worlds—preserving order while embracing innovation.

Making the content findable is important to knowledge management. Everyone is looking at AI. Everyone is getting mixed results. The main issue is that data science has not changed, and scientific content is very complex and needs more attention to get the most out of the new AI engines. This is not new for Access Innovations.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.